Multi-objective Wrapper-Based Feature Selection Using Binary Cuckoo Optimisation Algorithm: A Comparison Between NSGAII and NSGAIII

Usman, Ali Muhammad; Yusof, Umi Kalsom; Naim, Syibrah; Abdullahi, Ali Usman; Ahmed, Abubakar Mu’azu; Alomari, Osama Ahmad; Usman, Mohammed

doi:10.1007/978-3-030-80216-5_10

Cited by 4 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach can also be extended to increase processing constraints without introducing any new parameters, and the computational efficiency is greatly improved. Through this approach, the association state of the reference point set based on the reference point is updated adaptively in real time over multiple generations [ 39 ]. Based on these advantages, the authors of [ 40 ] proposed a multi-objective feature selection scheme with two special strategies to select the features of detection datasets.…”

Section: Nsga3mentioning

confidence: 99%

Towards Intelligent Attack Detection Using DNA Computing

Zeng

Zhao

Meng

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

In recent years, frequent network attacks have seriously threatened the interests and security of mankind. To address this threat, many detection methods have been studied, and some of them have achieved good results. However, with the development of network interconnection technology, massive network data have been produced, and considerable redundant information has been generated. At the same time, the frequently changing types of cyberattacks result in great difficulties in the collection of samples, resulting in a serious imbalance in the sample size of each attack type in the dataset. These two problems seriously reduce the robustness of the existing detection methods, but the existing research methods do not provide a good solution. To address these two problems, we define an unbalanced index and an optimal feature index to directly reflect the performance of the detection method in terms of overall accuracy, feature subset optimization and detection balance. Inspired by DNA computing, we propose intelligent attack detection based on DNA computing (ADDC). First, we design a set of regular encoding and decoding features based on DNA sequences and obtain a better subset of features through biochemical reactions. Second, nondominated ranking based on reference points is used to select individuals to form a new population to optimize the detection balance. Finally, a large number of experiments are carried out on four datasets to reflect real-world cyberattack situations. Experimental results show that compared with the most recent detection methods, our method can improve the overall accuracy of multiclass classification by up to 10%; the imbalance index decreased by 0.5, and 1.5 more attack types were detected on average; and the optimal index of the feature subset increased by 83.8%.

show abstract

Section: Nsga3mentioning

confidence: 99%